With over 2 million people in the United States alone known to abuse opioid-based pain medications, addiction to these drugs represents a significant public health concern. The mu-opioid receptor (MOR) is the member of the G protein-coupled receptor (GPCR) family that primarily mediates the actions of clinically used opioid- based analgesics. Based on extensive in vitro and in vivo work over the past decades, it has become increasingly clear that opioid ligands can produce different signaling, phosphorylation, desensitization, and internalization of MOR, with major implications for its physiological responses, including the development of analgesic tolerance. The recent suggestion that MOR oligomerization can modulate receptor binding, signaling, and/or trafficking further complicates our understanding of MOR-mediated function. The overall goal of the research proposed in this application is to reveal the molecular mechanisms underlying the observed functional selectivity at MOR. This information is important to ensure the fine-tuning of MOR signaling towards desired therapeutic pathways but away from those mediating adverse side effects, with the ultimate goal of discovering non-addictive analgesic agents. The computational research proposed in this grant application takes advantage of cutting-edge developments in theory and experiments to obtain rigorous mechanistic insight, at an unprecedented level of molecular detail, into the structure, spatio-temporal organization, and dynamics of MOR in the membrane, thus broadening current understanding of MOR biased agonism. Specifically, we will contribute structural and dynamic information regarding sparsely-populated states of MOR that are currently impossible or difficult to retrieve experimentally, thereby generating testable hypotheses of how, at the molecular level, different opioids induce differential oligomerization and signaling of MOR, leading to the specific behavioral effects of the drugs. Experimental validation of these computational predictions, to be attained through collaborations with independently funded laboratories, will advance our current understanding of fundamental basic mechanisms of MOR function, and pave the way to novel therapeutic strategies against drug abuse and addiction. The data that will emerge from this application will be added to other relevant recent information on GPCR oligomerization, and further populate our recently deployed GPCR-Oligomerization Knowledge Base system to continue to promote and support productive collaborations between computational and experimental scientists working on GPCRs involved in drug abuse.